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1 Type Package Title Big Data Analytics Version 0.1 Date Author Han Liu, Tuo Zhao Maintainer Han Liu Depends glmnet, Matrix, lattice, Package bigdata February 19, 2015 The big data package is a collection of scalable methods for large-scale data analysis. License GPL-2 Repository CRAN Date/Publication :55:53 NeedsCompilation no R topics documented: bigdata-package plot.stars print.stars Index 6 bigdata-package Big Data Analytics Details a collection of scalable methods for large-scale data analysis. 1

2 2 Package: bigdata Type: Package Version: 0.1 Date: License: GPL-2 LazyLoad: yes Han Liu, Tuo Zhao Maintainers: Han Liu References 1. Han Liu, Kathryn Roeder and Larry Wasserman. Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. Advances in Neural Information Processing Systems(NIPS), Stability Approach to Regularization Selection for Lasso Implements the Stability Approach to Regularization Selection (StARS) for Lasso Usage (x, y, rep.num = 20, lambda = NULL, nlambda = 100, lambda.min.ratio = 0.001, stars.thresh = 0.1, sample.ratio = NULL, alpha = 1, verbose = TRUE) Arguments x The n by d data matrix representing n observations in d dimensions y The n-dimensional response vector rep.num The number of subsampling for StARS. The default value is 20.

3 3 Details Value lambda A sequence of decresing positive numbers to control regularization. Typical usage is to leave the input lambda = NULL and have the program compute its own lambda sequence based on nlambda and lambda.min.ratio. Users can also specify a sequence to override this. Use with care - it is better to supply a decreasing sequence values than a single (small) value. nlambda The number of regularization paramters. The default value is 100. lambda.min.ratio The smallest value for lambda, as a fraction of the uppperbound (MAX) of the regularization parameter which makes all estimates equal to 0. The program can automatically generate lambda as a sequence of length = nlambda starting from MAX to lambda.min.ratio*max in log scale. The default value is stars.thresh sample.ratio The threshold of the variability in StARS. The default value is 0.1. The alternative value is Only applicable when criterion = "stars" The subsampling ratio. The default value is 10*sqrt(n)/n when n>144 and 0.8 when n<=144, where n is the sample size. alpha The tuning parameter for the elastic-net regression. The default value is 1 (lasso). verbose If verbose = FALSE, tracing information printing is disabled. The default value is TRUE. StARS selects the optimal regularization parameter based on the variability of the solution path. It chooses the least sparse graph among all solutions with the same variability. An alternative threshold 0.05 is chosen under the assumption that the model is correctly specified. In applications, the model is usually an approximation of the true model, 0.1 is a safer choice. The implementation is based on the popular package "glmnet". An object with S3 class "stars" is returned: path lambda opt.index opt.beta opt.lambda Variability The solution path of regression coefficients (in an d by nlambda matrix) The regularization parameters used in Lasso The index of the optimal regularization parameter. The optimal regression coefficients. The optimal regularization parameter. The variability along the solution path. Note This function can only work under the setting when d>1 Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, and Larry Wasserman Maintainers: Tuo Han Liu

4 4 plot.stars References 1.Han Liu, Kathryn Roeder and Larry Wasserman. Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. Advances in Neural Information Processing Systems, Jerome Friedman, Trevor Hastie and Rob Tibshirani. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, Vol.33, No.1, bigdata-package Examples #generate data x = matrix(rnorm(50*80),50,80) beta = c(3,2,1.5,rep(0,77)) y = rnorm(50) + x%*%beta #StARS for Lasso z1 = (x,y) summary(z1) plot(z1) #StARS for Lasso z2 = (x,y, stars.thresh = 0.05) summary(z2) plot(z2) #StARS for Lasso z3 = (x,y,rep.num = 50) summary(z3) plot(z3) plot.stars Plot function for S3 class "stars" Usage Visualize the solution path and plot the optimal solution by model selection ## S3 method for class stars plot(x,...) Arguments x An object with S3 class "stars"... System reserved (No specific usage)

5 print.stars 5 Han Liu and Tuo Zhao Maintainers: Han Liu print.stars Print function for S3 class "stars" Usage Print the information about the solution path length and the degree of freedom s along the solution path. ## S3 method for class stars print(x,...) Arguments x An object with S3 class "stars"... System reserved (No specific usage) Han Liu and Tuo Zhao Maintainers: Han Liu

6 Index bigdata-package, 1, 2, 2, 5 plot.stars, 4 print.stars, 5 6

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